Aviz Networks Blogs

The way data center networks operate is changing with the evolving nature of AI workloads. In a recent Aviz Networks podcast, Taylor Allison, Senior Product Marketing Manager at NVIDIA, spoke about changes that will happen in networking when it comes to large-scale AI training and inference. To do AI training, GPUs need to work in tandem, which requires a network that is able to communicate with low latency and stay synchronized. Once you increase the number of GPUs, even more, the existing networking technologies will start to fail. The networking solutions from NVIDIA that are optimized for AI workloads include Spectrum-X built for AI clusters over Ethernet and InfiniBand, another high-performance networking architecture for AI overclusters. The podcast also covered NVIDIA Air and the digital twin technology. Before deploying configurations and automation to the production environments, users can create virtual environments in which they can test configurations and automation. This g...

To investigate cyberattacks and suspicious activity - and to meet compliance regulations - security teams rely on evidence found within packets. However, traffic is traversing not only across their data centers, but also cloud platforms, campuses, and edges, making it challenging to capture every essential packet. Some typical problems that teams encounter are: * Overloaded packet capture systems unable to handle traffic spikes. * Irrelevant data obscuring important evidence. * Inconsistent historical records creating drag on investigations. * Higher infrastructure costs, without an increase in visibility. The Aviz Networks-Endace integrated solution makes this task simpler by guaranteeing that only optimized, high-fidelity traffic arrives at packet capture systems. Aviz Deep Network Observability (DNO) smart services efficiently aggregate, filter, dedup, and normalize traffic before directing it to Endace Always-On Packet Capture, which ingests, indexes, and permanently stores the tra...

Assembling AI infrastructure is significantly different than just installing servers and switches. Modern AI factories are highly interconnected and need to integrate networking, GPUs, storage, orchestration, and suites of applications. If configuration or integration issues appear in the deployed system, there will likely be major interruptions in the operation and costs associated with delays. Many companies struggle with: * A shortage of real-world physical lab testing. * Long validation/deployment cycles. * Discovering integration bugs at an inconvenient (read, costly!) late stage. * The hidden post-deployment rework needed to fix. That's where Aviz ONES on NVIDIA DSX Air helps deliver a better AI infrastructure build process: digital twin simulation. The idea is simple: instead of building, and then testing, why not design, simulate, and validate your entire AI networking environment beforehand? Engineers can create a model of their production-scale network, simulate traffic f...

In today's era, enterprise and telecom networks span much wider than a data center. These networks' traffic traverse through the cloud, edge, hybrid infrastructure, and wireless networks, giving network administrators a challenging time in gaining end-to-end visibility. Lack of smart traffic analysis has put security and network teams' fragmented views and increased incident response times. Typical issues faced are: The inability of getting visibility across a distributed environment. Replication of traffic which impacts data processing costs. Cost-intensive hardware appliances tied to proprietary technology. Lack of data to feed the AI-based observability platforms. Aviz Service Node v2.4 processes raw packet traffic to create machine-readable, application-aware metadata which is consumable by present-day observability platforms. ASN, through deep packet inspection (DPI) and application classification, enhances packet-aware traffic data with context so that AI/ ML can eff...

Modern AI infrastructure no longer only means networking together a stack of GPUs with a bunch of switches. The modern AI factory now relies on tightly optimized networking in which a slight configuration miss will affect performance, price or deployment timeline. Traditionally, this means it requires setting up and testing the network configuration in a physical lab which is expensive, time consuming and not easily scalable. Why, you ask? Because hardware testing alone can be cost prohibitive. Design cycles are serial and iterative, meaning designs and validation are split into distinct phases that take weeks or days to cycle through. When you're designing for the future, you have configuration misses which aren’t caught until you’ve rolled out production. It’s time to disrupt the cycle with digital twins. What NVIDIA AIR and Aviz ONES give you NVIDIA AIR provides an all-digital, virtualized AI network simulation where you can simulate all the switches, links, automation and confi...

Many hackers are able to breach various parts of the healthcare system because the SecOps team is unable to quickly understand how far an attacker can spread throughout multiple systems. Hospitals, clinics, telemedicine, cloud services, devices and facility systems create traffic on a common network; however, due to lack of central control over how traffic flows & devices in the Silverado system, many of these facilities have blind spots pertaining to the analysis of East to West traffic, devices that are not monitored (unmanaged) & mixed infrastructure (devices that do not have agent-based security monitoring). Some examples of this are: Almost all types of ransomware operated by transferring themselves around to other locations before they perform file&folder encryptions. A lot of the different device types and workloads out there within the Healthcare ecosystem will not be able to utilize endpoint agents. Packet level evidence will assist with identifying unusual flows o...